CN112990604A

CN112990604A - Computer-implemented method and computing device for reducing gas emissions

Info

Publication number: CN112990604A
Application number: CN202110432957.3A
Authority: CN
Inventors: 佟庆; 郭玥锋; 钱晶
Original assignee: Suzhou Innovation Research Institute Of Beijing University Of Aeronautics And Astronautics; Tsinghua University
Current assignee: Suzhou Innovation Research Institute Of Beijing University Of Aeronautics And Astronautics; Tsinghua University
Priority date: 2021-04-22
Filing date: 2021-04-22
Publication date: 2021-06-18
Anticipated expiration: 2041-04-22
Also published as: CN112990604B

Abstract

Embodiments of the present disclosure relate to computer-implemented methods and computing devices for reducing gas emissions. The method is used in parameter prediction related to carbon neutralization, and comprises the following steps: determining a predictor emission parameter for a candidate process combination of a plurality of candidate process combinations associated with the industry at an intermediate time, the plurality of candidate process combinations for reducing greenhouse gas emissions by the industry, the predictor emission parameter indicating an amount of greenhouse gas required to be emitted to produce a unit of product; determining a first predicted usage rate at which the candidate process combination will be used at the intermediate time; and determining a predicted parameter related to the industry at the intermediate time based on the predicted sub-emission parameter of the candidate process combination and the first predicted usage rate; wherein the intermediate time is between the base time and a target time at which the amount of greenhouse gas emissions of the industry is expected to be reduced below a predetermined threshold for achieving carbon neutralization. In this way, parameters associated with carbon neutralization can be accurately predicted.

Description

Computer-implemented method and computing device for reducing gas emissions

Technical Field

Embodiments of the present disclosure relate to the field of data processing, and more particularly, to computer-implemented methods, computing devices, and computer-readable storage media for reducing gas emissions.

Background

With increasing concern for global warming, "carbon neutralization" has become a current hotspot. For a particular industry requiring carbon emission reduction, carbon emission reduction can be achieved by using various processes for greenhouse gas emission reduction, as well as by reducing the total amount of products produced for that particular industry. The realization of carbon neutralization requires support from various data, especially from the process of carbon emission reduction and various emission reductions, which will help researchers to conduct research and policy makers to set up various policies, in the course of going to the vision year of achieving carbon neutralization. However, conventional top-down macro-analysis schemes do not efficiently and reliably determine such data, as carbon neutralization may be affected by a number of factors.

Disclosure of Invention

Embodiments of the present disclosure provide a computer-implemented method, computing device, and computer-readable storage medium for reducing gas emissions.

In a first aspect of the present disclosure, there is provided a computer-implemented method for reducing gas emissions for use in parameter prediction relating to carbon neutralization, the method comprising: determining a predictor emission parameter for a candidate process combination of a plurality of candidate process combinations associated with the industry at an intermediate time, the plurality of candidate process combinations for reducing greenhouse gas emissions by the industry, the predictor emission parameter indicating an amount of greenhouse gas required to be emitted to produce a unit of product; determining a first predicted usage rate at which the candidate process combination will be used at the intermediate time; and determining a predicted parameter related to the industry at the intermediate time based on the predicted sub-emission parameter of the candidate process combination and the first predicted usage rate; wherein the intermediate time is between the base time and a target time at which the amount of greenhouse gas emissions of the industry is expected to be reduced below a predetermined threshold for achieving carbon neutralization.

In a second aspect of the disclosure, there is provided a computing device comprising: a computing device, comprising: at least one processor; and a memory coupled with the at least one processor; wherein the memory has instructions stored therein which, when executed by the at least one processor, cause the at least one processor to perform a method according to the first aspect of the disclosure.

In a third aspect of the present disclosure, a non-transitory computer readable storage medium is provided having computer instructions stored thereon, wherein the computer instructions, when executed, cause a computer to perform a method according to the first aspect of the present disclosure.

The disclosed embodiments enable accurate prediction of parameters associated with carbon neutralization.

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the disclosure, nor is it intended to be used to limit the scope of the disclosure.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be apparent from the following more particular descriptions of exemplary embodiments of the disclosure as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the disclosure.

FIG. 1 schematically shows a schematic diagram of an example environment in accordance with an embodiment of the present disclosure.

Fig. 2 schematically shows a block diagram of a method for reducing gas emissions according to an embodiment of the present disclosure.

Fig. 3 schematically illustrates a schematic diagram of multiple routes for reducing gas emissions of an industry, according to an embodiment of the present disclosure.

Fig. 4 schematically illustrates a schematic of input data for the ammonia synthesis industry, in accordance with an embodiment of the disclosure.

Fig. 5 schematically illustrates a schematic of predicted parameters under baseline scenarios for the ammonia synthesis industry, in accordance with an embodiment of the present disclosure.

Fig. 6 schematically shows a schematic of predicted parameters for the synthetic ammonia industry in the scenario of carbon neutralization achieved in 2060, according to an embodiment of the present disclosure.

Fig. 7 schematically illustrates a schematic of predicted parameters for the synthetic ammonia industry in a scenario of carbon neutralization achieved in 2050, according to an embodiment of the disclosure.

Fig. 8 schematically illustrates a schematic of input data for the nitric acid industry, according to an embodiment of the present disclosure.

Fig. 9 schematically illustrates a schematic of predicted parameters under a baseline scenario for the nitric acid industry, according to an embodiment of the present disclosure.

Fig. 10 schematically shows a schematic of predicted parameters for the nitric acid industry in the context of achieving carbon neutralization in 2060 years, according to an embodiment of the present disclosure.

Fig. 11 schematically shows a schematic of predicted parameters for the nitric acid industry in the context of achieving carbon neutralization in 2050, according to an embodiment of the present disclosure.

Fig. 12 schematically shows a schematic of input data for the steel industry according to an embodiment of the present disclosure.

Fig. 13 schematically shows a schematic of predicted parameters for the steel industry at 2060 to achieve carbon neutralization, according to an embodiment of the present disclosure.

Fig. 14 schematically shows a visualization chart for representing some of the parameters in fig. 9 to 13.

FIG. 15 illustrates a schematic block diagram of an example computing device that can be used to implement embodiments of the present disclosure.

Detailed Description

The principles of the present disclosure will be described below with reference to a number of example embodiments shown in the drawings.

The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "a set of example embodiments". The term "another embodiment" means "a set of additional embodiments". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.

As discussed above, conventional schemes for macroscopic analysis cannot effectively determine the prediction parameters associated with each year between the years to vision. Moreover, the conventional solutions fail to take into account the variations of the various routes of the various industries that enable the reduction of emissions, and therefore fail to determine the prediction parameters related to the industries. Additionally, conventional approaches do not target carbon neutralization at the time of interest to determine various prediction parameters.

To address, at least in part, one or more of the above issues and other potential issues, an example embodiment of the present disclosure proposes a computer-implemented solution for reducing gas emissions. The solution enables to determine, for various technical routes for reducing gas emissions of various industries, intermediate objectives that the various technical routes need to achieve at various intermediate time nodes, by means of the objectives to be achieved in the vision year (sometimes also referred to herein as destination time). In this way, the time for achieving carbon neutralization can be used for carrying out reverse deduction, so that the predicted development change condition of each technical route can be determined, and the prediction parameters related to the industry can be accurately determined.

Example embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Example Environment

Fig. 1 illustrates a schematic diagram of an example environment 100 in which apparatus and/or methods according to embodiments of the disclosure may be implemented, according to embodiments of the disclosure. As depicted in fig. 1, the example environment 100 includes a computing device 105. Computing device 105 may be any device with computing capabilities. By way of non-limiting example, computing device 105 may be any type of stationary, mobile, or portable computing device, including but not limited to a desktop computer, laptop computer, notebook computer, netbook computer, tablet computer, multimedia computer, mobile phone, or the like; all or a portion of the components of computing device 105 may be distributed in the cloud. Computing device 105 contains at least a processor, memory, and other components typically found in a general purpose computer to implement computing, storage, communication, control, and the like functions.

The computing device 105 may receive input data 110 related to an industry and be configured to determine a predicted parameter 120 related to the industry based on the received input data 110 according to example methods described below. In some embodiments, the computing device 105 may be communicatively coupled to a database 115 that stores various data, and may retrieve corresponding data related to an industry from the database 115 as indicated in the received input data 110. In some embodiments, the database 115 may be a predetermined gas emissions database that maintains (e.g., stores and updates as needed) data related to various industries, such as data related to various industries collected at a base time.

In some embodiments, the data maintained in the database includes, but is not limited to, at least one of: one or more intermediate process combinations for reducing greenhouse gas (also sometimes referred to herein simply as gas) emissions for the industry, energy and/or feedstock used by the intermediate process combinations, predicted time of start of use of the intermediate process, gas class for which the intermediate process combination is directed, source of generation of the emitted gas (e.g., product production process or fuel combustion process), global warming potential of the emitted gas (also sometimes referred to herein simply as GWP), one or more intermediate processes included in the intermediate process combinations for reducing gas emissions (also sometimes referred to herein simply as abatement), various parameters associated with the intermediate process combinations at a base time. Various parameters associated with the intermediate process combination at the reference time include, but are not limited to, at least one of: a baseline sub-emission parameter for a combination of intermediate processes, a baseline usage rate for an intermediate process (also sometimes referred to herein as a first usage rate), an amount of said gas emissions that a process is capable of reducing (also sometimes referred to herein as a first parameter or an emission reduction factor), one or more end processes for reducing gas emissions for the industry (such as exhaust gas end treatment, CCS, CCU), a predicted time at which an end process will begin to be used, a rate at which an end process is capable of reducing gas emissions (also sometimes referred to simply as an emission reduction rate), predicted usage rates for various processes at a destination time, predicted start times at which various processes will begin to be used, and corresponding predicted total industry production in various scenarios, etc. For example, if the input data 110 indicates that the industry to be predicted is the steel industry, the computing device 105 may retrieve data related to the steel industry from a database. In some embodiments, the input data 110 may also include the above-described data directly, in which case the computing device 105 need not retrieve industry-related data from the database 115.

The foregoing describes an example environment in accordance with embodiments of the present disclosure. An example method according to an embodiment of the present disclosure will be described in detail below in conjunction with fig. 2-3. For ease of understanding, specific data mentioned in the following description are exemplary and are not intended to limit the scope of the present disclosure.

Example method

Fig. 2 schematically illustrates a block diagram of a method 200 for reducing gas emissions, in accordance with an embodiment of the present disclosure. For example, the method 200 may be performed by a computing device as shown in FIG. 1. It is to be understood that methods in accordance with embodiments of the present disclosure may also include additional acts not shown and/or may omit acts shown, as the scope of the present disclosure is not limited in this respect.

At block 202, the computing device 105 may determine a predicted sub-emission parameter for a candidate process combination of a plurality of candidate process combinations related to the industry at an intermediate time, wherein the plurality of candidate process combinations are for reducing greenhouse gas emissions of the industry, and the predicted sub-emission parameter indicates an amount of greenhouse gas required to be emitted to produce a unit of product. In some embodiments, the computing device may determine a predicted sub-emission parameter at an intermediate time for each of a plurality of candidate process combinations. In this context, the term "predictor emission parameters" may refer to specific candidate process combinations at target intermediate timesThe emission factor, which may refer to the amount of gas emitted per ton of product produced, in some embodiments, the predictor emission parameter may be signed

May be, for example, t greenhouse gas/t product (t represents tons), where i represents a target candidate process combination to be predicted from among the plurality of candidate process combinations.

In this context, the term "base time" may be the time at which various data/parameters for prediction are collected. It will be appreciated that the reference time may vary accordingly, for example, depending on the different data used in the database. Herein, the term "destination time" is sometimes also referred to as a vision time or vision year, at which time the amount of gas emissions of the industry is expected to be reduced below a predetermined threshold for achieving carbon neutralization. For example, the destination time may be 2060 or 2050 or other time when carbon neutralization is desired. In some embodiments, the predetermined threshold may be set to zero or near zero. In other words, the amount of greenhouse gas emissions by the industry will be zero or close to zero, and thus carbon neutralization is achieved. In this context, the term "intermediate time" may be any time or times between the reference time and the destination time at which various parameters related to the industry need to be predicted. In some embodiments, the plurality of intermediate times may be determined at predetermined intervals (also sometimes referred to herein as step sizes) between the reference time and the destination time. For example, in the case where the reference time is 2015 years and the destination time is 2060 years, the predetermined interval may be set to 5 years, but may also be set to 3 years, 1 year, or other time period.

For ease of understanding, the following detailed description will be made with reference to fig. 3. Fig. 3 schematically illustrates a schematic diagram 300 of multiple routes (also sometimes referred to herein as process combinations) for reducing gas emissions of an industry, according to an embodiment of the disclosure. In some embodiments, to determine the predictor emission parameters, the computing device may first obtain a plurality of candidate process combinations related to the industry, and obtain intermediate processes included by each candidate process combination for reducing gas emissions. For example, for a particular industry, after determining greenhouse gas emissions related to the particular industry, there may be a route combination 310 including a first route (indicated by reference numeral 311) to an nth route (indicated by reference numeral 311, where N is a positive integer greater than 1) for reducing the gas emissions of the particular industry. In some embodiments, the first route 311 may be, for example, a combination of processes based on source control (i.e., a source control route), which may include

processes

3111, 3112, …, 311N, and the nth route 312 may be, for example, a combination of processes based on non-source control (i.e., a non-source control route), which may include

processes

3121, 3122, …, 312N.

In this context, the term "source control based process combination" may refer to one or more production techniques in which the baseline sub-emission parameter is lowest at the baseline time, e.g., the first route 311 may be a production technique using low carbon energy or raw materials 313. The term "non-source control based process combination" may refer to routes (production technologies) other than the source controlled route in the route combination 310, e.g., the nth route 312 may be a production technology using conventional energy or raw materials 314. Each such route or combination of processes may achieve a production of the product and produce a corresponding reduced gas emission (e.g., the production/emission 315 for the first route 311, and the production/emission 316 for the nth route 312 in fig. 3). In some embodiments, the gas emitted by each of the routes in route set 310 may be further processed by end process 318 before being emitted to the atmosphere. It will be appreciated that the various process combinations/processes described above will vary from industry to industry, for example, example embodiments of various process combinations/processes determined by industry are listed herein in fig. 4, 8 and 12.

Referring back to fig. 2, in some embodiments, the baseline sub-emission parameter may be represented by an emission factor for each process combination at a baseline time. In some embodiments, the reference sub-emission parameter may be signed

Expressed in units of, for example, t greenhouse gas/t products, where i0 refers to a candidate combination of processes at a reference time. In some embodiments, the reference sub-emission parameter may be a preset constant, which is an actual value at a reference time, may be obtained through various statistics, and may be stored in a database. For example, for the ammonia synthesis industry, the baseline sub-emission parameter for a production technology for ammonia synthesis from hydrogen is the lowest (e.g., emission factor of 0, since it does not produce carbon dioxide), which can be a combination of processes based on source control. The baseline sub-emission parameters for ammonia synthesis from natural gas are also relatively low, which can also be a combination of processes based on source control. The synthesis of ammonia via coal-based processes in other process combinations has a higher baseline sub-emission parameter that can be used as a process combination based on non-source control.

In some embodiments, in the combination of processes where the baseline sub-emission parameter is zero, there will be no intermediate processes for emission reduction, and the end processes described in detail below. In some embodiments, for such a combination of processes, the computing device may determine whether there are elements in the input data that cause further reductions in the sub-emission parameters (intermediate processes or end processes). If so, the computing device may ignore such elements.

After determining one or more intermediate processes included in the process combination, the computing device may obtain a first parameter corresponding to such intermediate processes, the first parameter indicating an amount of gas emissions that the intermediate processes are capable of reducing. The first parameter is sometimes also referred to herein as an emission reduction factor, meaning that it is capable of reducing the emission factor to some extent. In some embodiments, the first parameter may be signed

May be, for example, kg greenhouse gas/t product (kg stands for kg), where i denotes the target candidate process combination to be predicted and j denotes the target intermediate process. Based on the acquired first parameter,And intermediate processes included in each candidate process combination, the computing device may utilize these emission reduction-related parameters to determine predicted sub-emission parameters at the intermediate times.

In some embodiments, the computing device will further determine predicted usage rates for each candidate process combination, and usage rates for each intermediate process included in each candidate process combination, for more accurate determination of the predicted sub-emission parameters. In particular, the computing device may obtain a baseline sub-emission parameter for the candidate process combination at a baseline time

And a first usage rate at which the included intermediate processes of the candidate process combination are used at the reference time is acquired. In some embodiments, the first usage rate may be signed

Is expressed, which represents the usage rate of the target intermediate process in the target candidate process combination to be predicted at the reference time, in other words, the rate of the products produced using the target intermediate process among the products produced using the target candidate process combination throughout the industry at the reference time. In some embodiments, the first usage rate may be a preset constant, which is an actual value at a reference time, may be obtained through various statistics, and may be stored in a database.

The computing device may then determine a predicted usage (also sometimes referred to herein as a second predicted usage) at the intermediate time that the intermediate process is to be used. In some embodiments, the second predicted usage may be used

Where i denotes the target candidate process combination to be predicted, j denotes the target intermediate process,

can represent the target to be predictedThe target intermediate process in the candidate process set will have a usage rate at an intermediate time, in other words, a rate of products produced using the target intermediate process among products produced using the target candidate process set throughout the industry at the intermediate time. In some embodiments, the predicted usage will be determined for each intermediate process in all candidate process combinations.

Based on the baseline sub-emission parameter, the first usage rate, the second predicted usage rate, and the first parameter, the computing device may calculate a predicted sub-emission parameter. For example, the computing device may utilize equation (1) below to calculate the predictor emission parameters associated with the candidate process combination based on the various parameters obtained and/or determined.

Where M is the number of intermediate processes included in the target candidate process combination i, M may be a positive integer greater than or equal to 1, and 1 ≦ j ≦ M.

In some embodiments, the second predicted usage may be determined by the computing device based on at least one of a reference usage of the intermediate process at a reference time and a first destination usage of the intermediate process at a destination time. In some embodiments, the computing device may determine one or more second predicted usage rates at one or more intermediate times between the reference time and the destination time based on the reference usage rate and the first destination usage rate through various usage rate prediction methods, such as a linear interpolation method. It will be appreciated that the usage prediction method may also employ interpolation methods based on other functions for determining one or more second predicted usage rates. In some embodiments, the first destination usage rate may be set to 100%, in other words, the intermediate process for emission reduction is used for all product production of the industry at the destination time. In some embodiments, technical constraints such as process economics, technical maturity, etc. may also be taken into account to determine the first destination usage and, in turn, one or more second predicted usage.

In some embodiments, in the event that some of the intermediate processes for emission reduction may not be currently in use, the computing device may further obtain a start time at which the intermediate processes begin to be used, and calculate a second predicted usage based on the start time, the destination time, a first destination usage of the intermediate processes at the destination time, and the intermediate time, wherein the start time is later than the base time and earlier than the destination time. In some embodiments, the computing device may determine one or more second predicted usage rates at one or more intermediate times between the start time and the destination time based on usage rates of intermediate processes at the start time and the first destination usage rate through various usage rate prediction methods, such as a linear interpolation method. It may be appreciated that the computing device may set one or more second predicted usage rates to 0 at one or more intermediate times between the base time and the start time. In some embodiments, at the start time, the usage of the intermediate process may be set to a lower value, e.g., 1%.

At block 204, computing device 105 may determine that at the intermediate time, the candidate process combination is to be used for a predicted usage (also sometimes referred to herein as a first predicted usage). In some embodiments, the computing device may determine, for each candidate process combination of the plurality of candidate process combinations, its first predicted usage at the intermediate time. In some embodiments, the first predicted usage may use

Where i represents a target candidate process combination to be predicted, which may represent the fraction of products produced using that candidate process combination among the products produced using the candidate process combination throughout the industry at intermediate times.

In some embodiments, the computing device may determine the first predicted usage using a process similar to the process described above for determining the second predicted usage. In particular, the first predicted usage may be determined by the computing device based on at least one of a reference usage of the candidate set of processes at a reference time (also sometimes referred to herein as a second usage), and a second destination usage of the candidate set of processes at a destination time. In some embodiments, the computing device may obtain a second usage at which the candidate process combination is to be used at the base time and obtain a second destination usage at which the candidate process combination is to be used at the destination time. Then, the computing device may calculate one or more first predicted usage rates at one or more intermediate times between the reference time and the destination time based on the second usage rate and the second destination usage rate through various usage rate prediction methods such as a linear interpolation method.

It will be appreciated that the usage prediction method may also employ interpolation methods based on other functions for determining the one or more first predicted usage rates. In some embodiments, the second-purpose usage rate may be set according to a reference sub-emission parameter, for example, the lower the reference sub-emission parameter, the higher the second-purpose usage rate may be. The source control-based process combination discussed above (with lower baseline sub-emission parameters) can achieve the best emission reduction results for reducing gas emissions. Accordingly, the predicted usage rates of such process combinations may be adjusted to be as high as possible to achieve the effect of maximizing such abatement techniques, and correspondingly, the predicted usage rates of other process combinations may be adjusted to be relatively low. It will be appreciated that at each time, the sum of the corresponding multiple (benchmark, prediction or destination) usage rates for the multiple candidate process combinations will be 1. In some embodiments, technical constraints such as process economics, technical maturity, etc. may also be taken into account to determine a suitable, as high as possible, second-purpose usage, and thus one or more first predicted usage.

In some embodiments, where some candidate process combinations for abatement (e.g., new low-carbon-energy-using process combinations) may not be currently in use, the computing device may further obtain a start time at which the candidate process combination begins to be used, and calculate a second predicted usage based on the start time, the destination time, a second destination usage of the process combination at the destination time, and an intermediate time, wherein the start time is later than the base time and earlier than the destination time. In some embodiments, the computing device may determine one or more first predicted usage rates at one or more intermediate times between the start time and the destination time based on the usage rates of the candidate process combinations at the start time and the second destination usage rate through the various usage rate prediction methods described above.

At block 206, the computing device 105 may determine a predicted parameter related to the industry at the intermediate time based on the predicted sub-emission parameter of the candidate process combination and the first predicted usage. In some embodiments, the prediction parameters may include at least one of: the predicted emissions parameter before end process treatment (which may be denoted by the symbol IEF' in some embodiments), the predicted emissions parameter after end process treatment (which may be denoted by the symbol IEF in some embodiments), the total gas emissions of the industry (which may be denoted by the symbol IEF in some embodiments)

Expressed), total gas emissions equivalent of the industry (which may be symbolized in some embodiments)

Expressed), consumption of each carbon-containing energy source (which may be symbolized in some embodiments)

Expressed), and the total amount of carbonaceous energy consumed by the industry (which may be represented by the symbol FC in some embodiments).

In some embodiments, the computing device may base the predictor emission parameters for each candidate process combination on (a)

) And a first predicted usage rate (

) To calculate a predicted emission parameter (IEF') at the intermediate time before being processed by the end process. For example, the computing device may calculate an industry-related IEF' as an industry-related predicted parameter (or at least a portion thereof) using equation (2) below based on the determined parameters described above.

Where N represents the number of candidate process combinations for the industry, and 1 ≦ i ≦ N.

In some embodiments, products produced via various intermediate processes for abatement will still be accompanied by a certain amount of gas emissions that can be treated using the end process. The end process may include at least one of: tail gas recovery technology, carbon capture and storage technology (CCS, which may be for carbon dioxide), carbon capture and utilization technology (CCU, which may be for carbon dioxide), catalytic decomposition technology for nitrogen oxides, and the like. The computing device may obtain a second parameter corresponding to an end process of the industry, the second parameter (also sometimes referred to herein as an emission reduction rate) indicating a rate at which the end process can reduce gas emissions, and in some embodiments, the second parameter may be signed

To indicate. The computing device may also determine one or more third predicted usages that the end process will be used at one or more intermediate times using a process similar to the determination of the first/second predicted usages described above, which may be symbolic in some embodiments

A representation that represents the rate of usage that the end process will have at intermediate times, in other words, at intermediate times, over the entire rowThe fraction of products produced using the end process among the products produced.

The computing device may then calculate an end-process-processed predicted emissions parameter (IEF) based on the predicted sub-emissions parameter, the first predicted usage, the second parameter, and the third predicted usage. In some embodiments, the computing device may first calculate IEF 'based on the predicted sub-emission parameter and the first predicted usage using equation (2) above, and then based on IEF', the second parameter(s) (IEF)

) And a third predicted usage rate (

) IEF is calculated as a business related prediction parameter (or at least a portion thereof) using equation (3) below.

Both the pre-end process processed predicted emissions parameter (IEF') and the end process processed predicted emissions parameter (IEF) may be indicative of the gas emission level of the industry at the intermediate time, with the plurality of possible routes and the effect of each emission reduction process included in the plurality of possible routes having been taken into account, and therefore such parameters can serve as an indicator of the magnitude of the prediction of the gas emission level of the industry at the intermediate time.

In some embodiments, the computing device may further obtain from the database a predicted total product volume (AD) for the industry, which may be in units of millions of tons, at one or more intermediate times. Based on the obtained predicted total product amount and the predicted end-process-processed emission parameters, the computing device may determine a total amount of gas (greenhouse gases such as carbon dioxide, nitrous oxide, ozone, methane, etc.) emissions for the industry

. In some embodiments, the computing device may calculate using equation (4) below

As a prediction parameter (or at least a portion thereof) associated with an industry.

In some embodiments, in the case where the emitted gas is not carbon dioxide, the computing device may further convert such total amount of gas emissions into a total equivalent amount of gas emissions in order to facilitate comparison of the determined predictive parameters for use by a user

I.e. the total amount of carbon dioxide gas emissions equivalent thereto, may be in units of million tons of carbon dioxide equivalents. In some embodiments, the computing device may calculate using equation (5) below

Where GWP is the global warming potential of the emitted gas, which may be related to the type of gas emitted.

In some embodiments, where carbonaceous energy sources, such as coal products, petroleum products, and natural gas products, need to be consumed as feedstock to, or as fuel to, the production process, the computing device may further determine the predicted energy consumption parameter at one or more intermediate times and/or at the time of interest as the industry-related predicted parameter (or at least a portion thereof). In some embodiments, if it is determined that the received input data includes a parameter indicative of a carbonaceous energy source, the computing device performs a process for determining a predicted energy consumption parameter. In some embodiments, the computing device may present a user with an associated user interface for selecting whether to calculate the predicted energy consumption parameter.

In some embodiments, the computing device may determine the predicted energy consumption parameter by the following process. Based on the predictor emission parameters as discussed above: (

) And parameters relating to the energy used (a)

) The computing device may determine a predicted sub-energy consumption parameter(s) associated with the combined use of energy by the candidate processes at the intermediate time(s) ((

). The predicted sub-energy consumption parameter may indicate an amount of energy required to be consumed to produce a unit of product, i.e., the energy required to be consumed to produce a unit of product (e.g., one ton) using the candidate process combination (e.g., one ton of standard coal), and may be in units of, for example, tons of standard coal per ton of product (tce/t of product). In some embodiments, the computing device may base the predictor emission parameters on (a), (b), (c), (d), (

) Calculated by using the following equation (6)

。

Wherein the parameters

Is the carbon dioxide emission coefficient (ton carbon dioxide/ton standard coal, tCO 2/tce) associated with the type of carbonaceous energy. The parameters may be determined based on the type of carbonaceous energy source

. For example, the carbon dioxide emission coefficient for coal products may be 2.64, the carbon dioxide emission coefficient for petroleum products may be 2.07, and the carbon dioxide emission coefficient for natural gas products may be 1.63.

In some embodiments, the use of an energy source as described above comprises at least one of: the carbonaceous energy source is used in a production process, such as chemical synthesis, or in a fuel combustion process that provides an energy source.

Using the determined predictor energy consumption parameter associated with the candidate process, the computing device may further based on the predictor energy consumption parameter(s) ((

) First predicted usage rate

And a predicted total product (AD) for the industry, calculating an energy consumption parameter FC. In some embodiments, the computing device may calculate using equations (7) and (8) below

Wherein

Representing the consumption of carbonaceous energy of type k (including but not limited to oil, coal, natural gas) used by the industry, which may be in units of million tons of standard coal, FC representing the total amount of carbonaceous energy consumed by the industry.

In some embodiments, the computing device may automatically select a corresponding parameter for a summation calculation, such as equation (7), based on the type of carbonaceous energy involved in each candidate route combination to determine the amount of energy consumption associated with that type of carbonaceous energy.

In some embodiments, the obtaining step described in this example method may utilize a predetermined gas emissions database, described above with reference to fig. 1, having data associated with various industries maintained at a base time. In some embodiments, the data may also be set by a user. In some embodiments, where data relating to a particular industry is stored in the database and the user also enters corresponding data, the computing device may perform a comparison between the two and notify the user through a prompt notification on the graphical interface if the difference between the two is greater than a threshold.

In some embodiments, the above example method may be performed for each intermediate time between the reference time and the destination time, for example to generate at least one of: a set of predicted emissions parameters corresponding to a set of intermediate times, and a set of predicted energy consumption parameters corresponding to a set of intermediate times.

In some embodiments, the above example method may also be performed for different destination times, for example to generate at least one of: multiple sets of predicted emissions parameters associated with different ones of the plurality of target times, and multiple sets of predicted energy consumption parameters associated with different ones of the plurality of target times. For example, carbon neutralization at two different destination times (e.g., 2060 and 2050) can be implemented as two different scenarios (e.g., carbon neutralization CN2060 implemented in 2060 and CN2050 implemented in 2050), and corresponding prediction parameters under the two different scenarios are determined. Additionally or alternatively, the respective prediction parameters under the reference scenario may also be determined as a comparison. In the reference scenario (BAU), the usage of all processes that can be used for emission reduction remains unchanged from the reference time. In some embodiments, the trend of the industry's total amount of product produced over time will also vary in different scenarios.

According to the method disclosed by the invention, the carbon neutralization can be realized at the target time, so that the utilization rate of various candidate route combinations related to the industry, the utilization rate of intermediate processes and/or end processes in the candidate route combinations can be determined to achieve the target time in the process of going to the target time, and various prediction parameters such as predicted emission parameters and predicted energy consumption parameters can be accurately determined, thereby being beneficial to making relevant policy decisions at the industry, local and even national level.

Example methods in accordance with embodiments of the present disclosure are described above. Example embodiments for determining respective prediction parameters for three different industries using example methods according to embodiments of the present disclosure will be described in detail below in conjunction with fig. 4-13. It will be appreciated that other industries not shown (a third industry such as the service industry) are equally applicable to the present example method. It will also be appreciated that aspects of the present disclosure are equally applicable to emission reduction of other gases in the industry, such as toxic and hazardous gases, so long as they are equally related to the time of interest and one or more process combinations or processes.

Exemplary embodiments for the Ammonia Synthesis industry

Fig. 4 schematically illustrates a schematic of input data 400 for the ammonia synthesis industry, in accordance with an embodiment of the disclosure. For example, various parameters in the input data 400 will be inputs to the method 200. In some embodiments, a portion of the input data 400 may be retrieved from a database. As shown in fig. 4, the input data may include a reference time 402 (set to 2015 years in the present embodiment), a target time (set to 2060 years or 2050 years in the present embodiment), a step size 406 for determining an intermediate time (i.e., a time interval, set to 5 years in the present embodiment), an industry 408 to be predicted (ammonia synthesis industry in the present embodiment), a category of gas emissions 410 (including gas emissions generated during industrial production of ammonia and gas emissions generated during fuel combustion for powering the process for producing ammonia in the present embodiment), a category of gas generated 412 (carbon dioxide in the present embodiment), and a GWP 414 (1 for carbon dioxide in the present embodiment).

The input data 400 may also includeA process combination (i.e., route) for producing a product (ammonia in this embodiment), and additionally or alternatively an end process-related process parameter 416. As shown in fig. 4, each process combination may be source-based control or non-source-based control, and may include one or more intermediate processes for emission reduction. The process parameters 416 may also include a reference usage rate and a reference emission factor at a reference time for each of the process combinations discussed above (e.g.,

) A reference usage rate of each intermediate process at a reference time (e.g.,

) And a destination usage rate at the destination time, an emission reduction factor corresponding to each intermediate process (e.g.,

) The emission reduction rate of the end process (e.g.,

) A baseline usage rate of the end process at the baseline time and a destination usage rate at the destination time, a time (e.g., start time) at which some or all of the process combinations and/or some or all of the intermediate processes and/or end processes begin to be used, and a carbon-containing energy source used by each process (if involved). The input data 400 may also include product yield parameters 418, which may include the predicted total production of ammonia (e.g., AD in million tons) for the ammonia synthesis industry at different times under different scenarios to be predicted (e.g., baseline scenario, carbon neutralization CN2060 achieved in 2060 and carbon neutralization CN2050 achieved in 2050).

Fig. 5 schematically illustrates a schematic 500 of predicted parameters under baseline scenarios for the ammonia synthesis industry, in accordance with an embodiment of the present disclosure. The prediction parameters may be based on the input data 400 in FIG. 4, used by the computing device to determine the parameters described above

、IEF'、IEF、

、

、

、

FC, various example methods. Since the ammonia synthesis industry may not only use carbonaceous energy sources as the feedstock for ammonia synthesis, but may also use carbonaceous energy sources for combustion as the energy supply source for ammonia synthesis, the predicted parameters may include predicted emissions parameters 524 and predicted energy consumption parameters 528 for different times. It will be appreciated that the computing device may utilize the predicted total production of ammonia at different times related to the BAU in the product production parameter 418 in FIG. 4 for determination of the prediction parameter.

Fig. 6 schematically shows a schematic 600 of predicted parameters for the ammonia synthesis industry in the scenario of carbon neutralization achieved in 2060, according to an embodiment of the present disclosure. The prediction parameters may be based on the input data 400 in FIG. 4, used by the computing device to determine the parameters described above

、IEF'、IEF、

、

、

、

、FC、

、

、

To various example methods of (a). Since the ammonia synthesis industry may not only use carbonaceous energy as a feedstock for ammonia synthesis, but may also use carbonaceous energy for combustion as a source of energy for ammonia synthesis, and the usage of individual process combinations and/or intermediate and/or end processes at different times may need to be predicted, the predicted parameters may include predicted usage 622, predicted emissions 624, and predicted energy consumption 628 at different times. It will be appreciated that the computing device may utilize the predicted total production of ammonia at different times in relation to CN2060 in product production parameter 418 in fig. 4 for the determination of the prediction parameter.

Fig. 7 schematically illustrates a schematic 700 of predicted parameters for the synthetic ammonia industry under the scenario of carbon neutralization achieved in year 2050, according to an embodiment of the disclosure. The prediction parameters may be based on the input data 400 in FIG. 4, used by the computing device to determine the parameters described above

、IEF'、IEF、

、

、

、

、FC、

、

、

To various example methods of (a). Since the ammonia synthesis industry may not only use carbonaceous energy sources as raw material for ammonia synthesis, but may also use carbonaceous energy sources for combustion as a source of energy for ammonia synthesis, and the usage of individual process combinations and/or intermediate and/or end processes at different times may need to be predicted, the predicted parameters may include predicted usage 722, predicted emissions 724, and predicted energy consumption 728 at different times. It will be appreciated that the computing device may utilize the predicted total production of ammonia at different times in relation to CN2050 in product production parameter 418 in fig. 4 for determination of the prediction parameter.

Exemplary embodiments for nitric acid industry

Fig. 8 schematically illustrates a schematic of input data 800 for the nitric acid industry, according to an embodiment of the present disclosure. For example, various parameters in the input data 800 will be inputs to the method 200 described above. In some embodiments, a portion of the input data 800 may be retrieved from a database. As shown in fig. 8, the input data may include a reference time 802 (set to 2015 years in the present embodiment), a target time (set to 2060 years or 2050 years in the present embodiment), a step size 806 for determining an intermediate time (i.e., a time interval, set to 5 years in the present embodiment), an industry 808 to be predicted (nitric acid industry in the present embodiment), a category 810 of generated gas emissions (generated gas emissions during industrial production in the present embodiment), a category 812 of generated gas (dinitrogen oxide in the present embodiment), and a GWP 814. It will be appreciated that unlike the above exemplary embodiment of the ammonia synthesis industry, for the nitric acid industry, the greenhouse gases produced during the production of nitric acid will include nitrous oxide, which will have a different GWP than carbon dioxide.

The input data 800 may also include process parameters 816 related to the process combination (i.e., route) used to produce the product (nitric acid in this embodiment), and additionally or alternatively, the end process. As shown in fig. 4, each process combination may be source-based control or non-source-based control, and may include one or more intermediate processes for emission reduction. Process parameters 816 may also include a reference usage rate and a reference emission factor at a reference time for each process combination discussed above (e.g.,

) The emission reduction rate of the end process (e.g.,

) A reference usage rate of the end process at the reference time and a destination usage rate at the destination time, a portion or all of the process combinations, and/or a portion or all of the intermediate processes and/or a time (e.g., start time) at which the end process begins to be used. Input data 800 may also include product yield parameters 818, which may include the predicted total yield of nitric acid (e.g., AD in million tons) for the nitric acid industry at different times under different scenarios to be predicted (e.g., baseline scenario, carbon neutralization CN2060 achieved in 2060 and carbon neutralization CN2050 achieved in 2050).

Fig. 9 schematically illustrates a diagram 900 of predicted parameters under a baseline scenario for the nitric acid industry, in accordance with an embodiment of the present disclosure. The prediction parameters canTo be used by a computing device for determining based on the input data 800 in FIG. 8

IEF'、IEF、

、

、

、

FC, various example methods. The predicted parameters may include predicted emissions parameters 924 for different times. It will be appreciated that the computing device may utilize the predicted total yield of nitric acid at different times related to BAU in product yield parameter 818 in FIG. 8 for determination of the predicted parameter.

Fig. 10 schematically shows a schematic diagram 1000 of predicted parameters for the nitric acid industry in the context of achieving carbon neutralization in 2060 years, according to an embodiment of the present disclosure. The prediction parameters may be based on the input data 800 in FIG. 8, used by the computing device to determine the parameters described above

、IEF'、IEF、

、

、

、

、

To various example methods of (a). Since the usage of various process combinations and/or intermediate and/or end processes at different times needs to be predicted, the predicted parameters may include predicted usage 1022 and predicted emissions parameters 1024 at different times. It will be appreciated that the computing apparatus may utilize the predicted total yield of nitric acid at different times in relation to CN2060 in product yield parameter 818 in fig. 8 for the determination of the predicted parameter.

Fig. 11 schematically shows a schematic 1100 of predicted parameters for the nitric acid industry in the context of achieving carbon neutralization in 2050, according to an embodiment of the disclosure. The prediction parameters may be based on the input data 800 in FIG. 8, used by the computing device to determine the parameters described above

、IEF'、IEF、

、

、

、

、

To various example methods of (a). Since the usage of various process combinations and/or intermediate and/or end processes at different times needs to be predicted, the predicted parameters may include predicted usage 1122 and predicted emissions parameters 724 at different times. It will be understood that the computing device may utilize the predicted total production of ammonia at different times in relation to CN2050 in product yield parameter 818 in fig. 8Amount for use in the determination of the prediction parameter.

Example embodiments for the Steel industry

Fig. 12 schematically shows a schematic of input data 1200 for the steel industry according to an embodiment of the disclosure. For example, various parameters in the input data 1200 will be inputs to the method 200. In some embodiments, a portion of the input data 1200 may be retrieved from a database. As shown in fig. 12, the input data may include a reference time 1202 (set to 2015 years in the present embodiment), a target time (set to 2060 years or 2050 years in the present embodiment), a step 1206 for determining an intermediate time (i.e., a time interval, set to 5 years in the present embodiment), an industry 1208 (steel industry in the present embodiment) to be predicted, a category 1210 of generated gas emissions (including, in the present embodiment, gas emissions generated in an industrial production process for producing steel and gas emissions generated in a fuel combustion process for energizing a process for producing steel), a category 1212 of generated gas (carbon dioxide in the present embodiment), and a GWP 1214 (1 for carbon dioxide in the present embodiment).

The input data 1200 may also include process parameters 1216 associated with the process combination (i.e., route) used to produce the product (steel in this embodiment), and additionally or alternatively the end process. As shown in fig. 12, each process combination may be source-based control or non-source-based control, and may include one or more intermediate processes for emission reduction. Process parameters 1216 may also include a reference usage and a reference emission factor at a reference time for each process combination discussed above (e.g.,

) The emission reduction rate of the end process (e.g.,

) A baseline usage rate of the end process at the baseline time and a destination usage rate at the destination time, a time (e.g., start time) at which some or all of the process combinations and/or some or all of the intermediate processes and/or end processes begin to be used, and a carbon-containing energy source used by each process (if involved). The input data 1200 may also include product yield parameters 1218, which may include the predicted total production of steel (e.g., AD in million tons) for the steel industry at different times under different scenarios to be predicted (e.g., baseline scenario, CN2060 achieved carbon neutralization and CN2060 achieved carbon neutralization 2050 in 2060).

Fig. 13 schematically shows a schematic diagram 1300 of predicted parameters for the steel industry at 2060 that achieved carbon neutralization, according to an embodiment of the present disclosure. The prediction parameters may be based on the input data 1200 in FIG. 12, used by the computing device to determine the parameters described above

、IEF'、IEF、

、

、

、

、FC、

、

、

To various example methods of (a). Since the steel industry may not only use carbonaceous energy as a raw material for steel, but may also use carbonaceous energy for combustion as an energy supply for steel, and the usage of individual process combinations and/or intermediate and/or end processes at different times needs to be predicted, the predicted parameters may include predicted usage 1322, predicted emissions parameters 1324, and predicted energy consumption parameters 1328 at different times. It will be understood that the computing device may utilize the predicted total production of steel at different times in relation to CN2060 in the product production parameter 1218 in fig. 12 for the determination of the prediction parameter. In a similar manner, prediction parameters such as in the case of the BAU and CN2050 scenarios may also be determined.

Visualization of prediction parameters

The present disclosure also provides for visualization of the prediction parameters. Fig. 14 schematically illustrates a visualization chart 1400 for representing some of the parameters in fig. 9-13. The visualization chart 1400 may include a graph 1432 showing the variation of the predicted emissions parameter IEF for the nitric acid industry under three different scenarios (i.e., BAU, CN2060, CN 2050). The visualization chart 1400 may also include a bar chart 1434 showing the variation in the predicted yield of the nitric acid industry under three different scenarios (i.e., BAU, CN2060, CN 2050). The visualization chart 1400 may also include a comparison chart 1436 showing a comparison of the total amount of industrial greenhouse gas emissions from the nitric acid industry in three different scenarios (i.e., BAU, CN2060, CN 2050), from which comparison chart 1436 it can be seen that the process combinations taken by CN2060 and CN2050 each significantly reduce gas emissions relative to BAU. The visualization chart 1400 may also include a graph 1438 showing the variation of the predicted emission parameter IEF' of the nitric acid industry prior to treatment with the end process in three different scenarios (i.e., BAU, CN2060, CN 2050).

In this way, various prediction parameters can be intuitively presented, thereby facilitating use by a user of the prediction parameters. It will be appreciated that the above shows only a visualization of a portion of the predicted parameters, and that visualizations of parameters in the various tables described herein or other parameters that can be obtained in accordance with aspects of the present disclosure are also within the scope of the present disclosure.

Example computing device

FIG. 15 illustrates a schematic block diagram of an example computing device 1500 that can be used to implement embodiments of the present disclosure. For example, computing device 1500 may be used to implement computing device 105 shown in fig. 1. As shown, computing device 1500 includes a Central Processing Unit (CPU) 1501 that can perform various suitable actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM) 1502 or loaded from a storage unit 1508 into a Random Access Memory (RAM) 1503. In the RAM, various programs and data required for operation of the computing device 1500 may also be stored. The CPU, ROM, and RAM are connected to each other by a bus 1504. An input/output (I/O) interface 1505 is also connected to bus 1504.

A number of components in computing device 1500 connect to I/O interface 1505, including: an input unit 1506 such as a keyboard, a mouse, and the like; an output unit 1507 such as various types of displays, speakers, and the like; a storage unit 1508, such as a magnetic disk, optical disk, or the like; and a communication unit 1509 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 1509 allows the computing device 1500 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks.

The central processing unit 1501 executes the respective methods and processes described above, such as the method 200. For example, in some embodiments, method 200 may be implemented as a computer software program or computer program object tangibly embodied in a machine-readable medium, such as storage unit 1508. In some embodiments, part or all of the computer program may be loaded and/or installed onto computing device 1500 via ROM and/or communications unit 1509. When the computer program is loaded into RAM and executed by a CPU, it may perform one or more steps of any of the methods described above. Alternatively, in other embodiments, the CPU may be configured to perform any of the above methods by any other suitable means (e.g., by means of firmware).

The present disclosure may be methods, apparatus, systems, and/or computer program objects. The computer program object may include a computer-readable storage medium having computer-readable program instructions embodied thereon for carrying out various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, any non-transitory memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program objects according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program objects according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A computer-implemented method for reducing gas emissions for use in parameter prediction related to carbon neutralization, the method comprising:

determining a predictor emission parameter for a candidate process combination of a plurality of candidate process combinations associated with an industry at an intermediate time, the plurality of candidate process combinations for reducing greenhouse gas emissions by the industry, the predictor emission parameter indicating an amount of greenhouse gas required to be emitted to produce a unit of product;

determining a first predicted usage rate at which the candidate process combination will be used at the intermediate time; and

determining a predicted parameter related to the industry at the intermediate time based on the predicted sub-emission parameter of the candidate process combination and the first predicted usage rate;

wherein the intermediate time is between a base time and a target time at which the amount of greenhouse gas emissions of the industry is expected to be reduced below a predetermined threshold for achieving carbon neutralization.

2. The method of claim 1, wherein determining the predictor emission parameter comprises:

obtaining intermediate processes included in the candidate process combination for reducing the greenhouse gas emission;

obtaining a first parameter corresponding to the intermediate process, the first parameter indicating an amount of the greenhouse gas emissions that the intermediate process is capable of reducing; and

calculating the predictor emission parameter based at least on the first parameter.

3. The method of claim 2, wherein calculating the predictor emission parameters comprises:

obtaining a reference sub-emission parameter of the candidate process combination at the reference time;

acquiring a first usage rate at which the intermediate process is used at the reference time;

determining a second predicted usage rate at the intermediate time at which the intermediate process will be used; and

calculating the predicted sub-emission parameter based on the baseline sub-emission parameter, the first usage rate, a second predicted usage rate, and the first parameter.

4. The method of claim 3, wherein determining the second predicted usage rate comprises:

acquiring the starting time of the intermediate process used; and

calculating the second predicted usage rate based on the start time, the destination time, a first destination usage rate of the intermediate process at the destination time, and the intermediate time, wherein the start time is later than the base time and earlier than the destination time.

5. The method of claim 1, wherein obtaining the first predicted usage rate comprises:

acquiring a second utilization rate of the candidate process combination used at the reference time;

determining a second destination usage rate at which the candidate process combination will be used at the destination time; and

calculating the first predicted usage based on the second usage and the second destination usage.

6. The method of claim 1, wherein determining the predicted parameter comprises determining a predicted emission parameter, comprising:

obtaining a second parameter corresponding to an end process of the industry, the second parameter indicating a rate at which the end process can reduce the greenhouse gas emissions;

determining a third predicted usage rate at which the end process will be used at the intermediate time; and

calculating the predicted emissions parameter based on the predicted sub-emissions parameter, the first predicted usage rate, the second parameter, and the third predicted usage rate.

7. The method of claim 1, wherein determining the predicted parameter comprises determining a predicted energy consumption parameter, determining the energy consumption parameter comprising:

determining a predictor energy consumption parameter associated with using the energy source in combination with the candidate process at the intermediate time based on the predictor emission parameter and a parameter of the energy source used, the predictor energy consumption parameter indicative of an amount of energy source required to be consumed to produce a unit of product; and

calculating the energy consumption parameter based on the predicted sub-energy consumption parameter, the first predicted usage rate, and a predicted total product amount of the industry.

8. The method of claim 4, wherein the first destination usage is set to 100%.

9. The method according to claim 5, wherein the second destination usage rate is set according to the baseline sub-emission parameter.

10. The method of any one of claims 2 to 6, wherein the obtaining step utilizes a predetermined gas emissions database maintaining data relating to each industry at the base time.

11. The method of any of claims 1 to 9, further comprising:

determining the prediction parameters comprises generating at least one of:

a set of predicted emission parameters corresponding to a set of intermediate times,

a set of predicted energy consumption parameters corresponding to a set of intermediate times,

sets of predicted emission parameters associated with different ones of the plurality of target times, an

A plurality of sets of predicted energy consumption parameters associated with a plurality of different destination times.

12. A computing device, comprising:

at least one processor; and

a memory coupled with the at least one processor; wherein the content of the first and second substances,

the memory has instructions stored therein that, when executed by the at least one processor, cause the at least one processor to perform the method of any of claims 1-11.

13. A non-transitory computer readable storage medium having computer instructions stored thereon, wherein the computer instructions, when executed, cause a computer to perform the method of any of claims 1-11.